Exploring the route choice of slime mold in a ballistic logic gate maze in different conditions
DOI:
https://doi.org/10.61173/dgzmjm26Keywords:
Physarum polycephalum, ballistic, logic gate, unconventional computerAbstract
This study investigates the factors affecting the smart behavior of slime mold, a single-celled simple organism, in a ballistic logic gate maze. The slime mold can choose between the two output sites marked by food and act as a decision-making logic gate that can be applied in biological computing and other logic-requiring operations. The molds were hypothesized to have direction-determining abilities, which allowed them to choose a straight path starting from a dead-end and grow without turning until another dead-end was reached. Experiments as primary research are conducted for six groups of slime mold, varying in food type on the output site, and the stress level of the mold is determined by the fed/unfed status. Incorporating evidence from time-stamped photographs recorded over a 20-hour period, our analysis shows a strong correlation between the speeds of growing/food covering and the slime mold’s stress level. We concluded that the unfed slime mold grows slower on agar gel but covers the food faster and thus has a greater overall speed in completing the logic gate. The speed of covering the food is the fastest on oats, followed by yogurt, and is the slowest on apple slices. The results of the success rate regarding the straight-through growing behaviour of the slime mold are lower than expected. While accessing limited sample size during the experiment, this study questions the reliability of the slime mold direction pattern and recommends further research and confirmations on the issue.
References
[1] Adamatzky, A. (2010). Slime mould logical gates: exploring ballistic approach. arXiv preprint arXiv:1005.2301.
[2] Adamatzky, A. (2015). Slime mould processors, logic gates and sensors. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2046), 20140216.
[3] Blakeslee, S. (2004). The CRAAP test. Loex Quarterly, 31(3), 4.
[4] Boussard, A., Fessel, A., Oettmeier, C., Briard, L., Döbereiner, H. G., and Dussutour, A. (2021). Adaptive behaviour and learning in slime moulds: the role of oscillations. Philosophical Transactions of the Royal Society B, 376(1820), 20190757.
[5] Halvorsrud, R., and Wagner, G. (1998). Growth patterns of the slime mold Physarum on a nonuniform substrate. Physical Review E, 57(1), 941.
[6] Howard, F. L. (1931). The life history of Physarum polycephalum. American journal of botany, 116-133.
[7] Krzywda, A., Petelenz, E., Michalczyk, D., and Płonka, P. (2008). Sclerotia of the acellular (true) slime mould Fuligo septica as a model to study melanization and anabiosis. Cellular and Molecular Biology Letters, 13(1), 130-143.
[8] Latty, T., and Beekman, M. (2011). Speed–accuracy tradeoffs during foraging decisions in the acellular slime mould Physarum polycephalum. Proceedings of the Royal Society B: Biological Sciences, 278(1705), 539-545.
[9] Nakagaki, T., Yamada, H. (2000). Rhythmic Contraction and its Fluctuations in AN Amoeboid Organism of the Physarumplasmodium. In Quantum Information II (pp. 107- 123).
[10] Nakagaki, T., Yamada, H., Tóth, Á. (2000). Maze-solving by an amoeboid organism. Nature, 407(6803), 470-470.
[11] Nakagaki, T., Yamada, H., Ueda, T. (2000). Interaction between cell shape and contraction pattern in the Physarum plasmodium. Biophysical chemistry, 84(3), 195-204.
[12] Nakagaki, T. (2001). Smart behavior of true slime mold in a labyrinth. Research in Microbiology, 152(9), 767-770.
[13] Raper, K. B. (1951). Isolation, cultivation, and conservation of simple slime molds. The Quarterly review of biology, 26(2), 169-190.
[14] Rawshan, T. and Davidoff, E. (2023) The effect of different food sources on slime mold growth and memory, Scipedia. Available at: https://www.scipedia.com/public/Scipedia_et_ al_2023a (Accessed: 05 December 2024).
[15] Reid, C. R., Latty, T., Dussutour, A., Beekman, M. (2012). Slime mold uses an externalized spatial “memory” to navigate in complex environments. Proceedings of the National Academy of Sciences, 109(43), 17490-17494.
[16] Yoshimoto, Y., and Kamiya, N. (1984). ATP-and calciumcontrolled contraction in a saponin model of Physarum polycephalum. Cell structure and function, 9(2), 135-141.
[17] Youvan, D.C. (2024). Biological inspiration for Al: Analogies Between Slime Mold Behavior and Decentralized Artificial Intelligence Systems. [online] doi:https://doi. org/10.13140/RG.2.2.22037.54247.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.

This work is licensed under a Creative Commons Attribution 4.0 International License.
